Telco完整项目展示

涵盖Tableau可视化、PPT报告、HTML可视化分析及Python代码解析

Tableau可视化展示

Tableau Dashboard Screenshot

点击图片查看在线Tableau Dashboard

PPT报告展示

Telco业分析报告

Telco Customer Churn Report

Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations7043
Missing cells0
Missing cells (%)0.0%
Duplicate rows20
Duplicate rows (%)0.3%
Total size in memory694.8 KiB
Average record size in memory101.0 B

Variable types

Categorical7
Numeric3
Boolean21

Alerts

Dataset has 20 (0.3%) duplicate rowsDuplicates
Contract_Two year is highly overall correlated with tenureHigh correlation
DeviceProtection_No internet service is highly overall correlated with InternetService_No and 6 other fieldsHigh correlation
DeviceProtection_Yes is highly overall correlated with MonthlyCharges and 1 other fieldsHigh correlation
InternetService_Fiber optic is highly overall correlated with MonthlyChargesHigh correlation
InternetService_No is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
MonthlyCharges is highly overall correlated with DeviceProtection_No internet service and 14 other fieldsHigh correlation
MultipleLines_No phone service is highly overall correlated with MonthlyCharges and 1 other fieldsHigh correlation
MultipleLines_Yes is highly overall correlated with MonthlyChargesHigh correlation
OnlineBackup_No internet service is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
OnlineBackup_Yes is highly overall correlated with TotalChargesHigh correlation
OnlineSecurity_No internet service is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
PhoneService is highly overall correlated with MonthlyCharges and 1 other fieldsHigh correlation
StreamingMovies_No internet service is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
StreamingMovies_Yes is highly overall correlated with MonthlyCharges and 2 other fieldsHigh correlation
StreamingTV_No internet service is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
StreamingTV_Yes is highly overall correlated with MonthlyCharges and 2 other fieldsHigh correlation
TechSupport_No internet service is highly overall correlated with DeviceProtection_No internet service and 6 other fieldsHigh correlation
TotalCharges is highly overall correlated with DeviceProtection_Yes and 5 other fieldsHigh correlation
tenure is highly overall correlated with Contract_Two year and 1 other fieldsHigh correlation
PhoneService is highly imbalanced (54.1%) Imbalance
MultipleLines_No phone service is highly imbalanced (54.1%) Imbalance

Reproduction

Analysis started2025-08-09 10:12:53.387535
Analysis finished2025-08-09 10:12:56.064012
Duration2.68 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
1
3555 
0
3488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

Length

2025-08-09T20:12:56.113569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:56.182400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3555
50.5%
0 3488
49.5%

SeniorCitizen
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
0
5901 
1
1142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Length

2025-08-09T20:12:56.237179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:56.299014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5901
83.8%
1 1142
 
16.2%

Partner
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
0
3641 
1
3402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Length

2025-08-09T20:12:56.355903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:56.418693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Most occurring characters

ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3641
51.7%
1 3402
48.3%

Dependents
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
0
4933 
1
2110 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

Length

2025-08-09T20:12:56.472371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:56.531968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

Most occurring characters

ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4933
70.0%
1 2110
30.0%

tenure
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.371149
Minimum0
Maximum72
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2025-08-09T20:12:56.598078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.559481
Coefficient of variation (CV)0.75868426
Kurtosis-1.3873716
Mean32.371149
Median Absolute Deviation (MAD)22
Skewness0.23953975
Sum227990
Variance603.16811
MonotonicityNot monotonic
2025-08-09T20:12:56.679898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
8 123
 
1.7%
70 119
 
1.7%
Other values (63) 4778
67.8%
ValueCountFrequency (%)
0 11
 
0.2%
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

PhoneService
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
1
6361 
0
682 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

Length

2025-08-09T20:12:57.297370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:57.355487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

Most occurring characters

ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6361
90.3%
0 682
 
9.7%

PaperlessBilling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
1
4171 
0
2872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

Length

2025-08-09T20:12:57.405963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:57.464280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

Most occurring characters

ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4171
59.2%
0 2872
40.8%

MonthlyCharges
Real number (ℝ)

High correlation 

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.761692
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2025-08-09T20:12:57.525435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.090047
Coefficient of variation (CV)0.46462725
Kurtosis-1.2572597
Mean64.761692
Median Absolute Deviation (MAD)24.05
Skewness-0.22052443
Sum456116.6
Variance905.41093
MonotonicityNot monotonic
2025-08-09T20:12:57.601598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 61
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
20 43
 
0.6%
19.7 43
 
0.6%
19.65 43
 
0.6%
19.55 40
 
0.6%
20.15 40
 
0.6%
19.75 39
 
0.6%
Other values (1575) 6601
93.7%
ValueCountFrequency (%)
18.25 1
 
< 0.1%
18.4 1
 
< 0.1%
18.55 1
 
< 0.1%
18.7 2
 
< 0.1%
18.75 1
 
< 0.1%
18.8 7
0.1%
18.85 5
0.1%
18.9 2
 
< 0.1%
18.95 6
0.1%
19 7
0.1%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%
117.8 1
< 0.1%
117.6 1
< 0.1%
117.5 1
< 0.1%
117.45 1
< 0.1%
117.35 1
< 0.1%

TotalCharges
Real number (ℝ)

High correlation 

Distinct6531
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2279.7343
Minimum0
Maximum8684.8
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2025-08-09T20:12:57.677189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.6
Q1398.55
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8684.8
Interquartile range (IQR)3388.05

Descriptive statistics

Standard deviation2266.7945
Coefficient of variation (CV)0.99432397
Kurtosis-0.22857981
Mean2279.7343
Median Absolute Deviation (MAD)1223.1
Skewness0.96323465
Sum16056169
Variance5138357.2
MonotonicityNot monotonic
2025-08-09T20:12:57.748668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
0.2%
20.2 11
 
0.2%
19.75 9
 
0.1%
20.05 8
 
0.1%
19.9 8
 
0.1%
19.65 8
 
0.1%
45.3 7
 
0.1%
19.55 7
 
0.1%
20.15 6
 
0.1%
20.25 6
 
0.1%
Other values (6521) 6962
98.8%
ValueCountFrequency (%)
0 11
0.2%
18.8 1
 
< 0.1%
18.85 2
 
< 0.1%
18.9 1
 
< 0.1%
19 1
 
< 0.1%
19.05 1
 
< 0.1%
19.1 3
 
< 0.1%
19.15 1
 
< 0.1%
19.2 4
 
0.1%
19.25 3
 
< 0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%
8547.15 1
< 0.1%
8543.25 1
< 0.1%
8529.5 1
< 0.1%
8496.7 1
< 0.1%
8477.7 1
< 0.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
0
5174 
1
1869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Length

2025-08-09T20:12:57.811919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-09T20:12:57.869614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring characters

ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5174
73.5%
1 1869
 
26.5%

InternetService_Fiber optic
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3947 
True
3096 
ValueCountFrequency (%)
False 3947
56.0%
True 3096
44.0%
2025-08-09T20:12:57.923400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

InternetService_No
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:57.977632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5570 
True
1473 
ValueCountFrequency (%)
False 5570
79.1%
True 1473
 
20.9%
2025-08-09T20:12:58.032011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Contract_Two year
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5348 
True
1695 
ValueCountFrequency (%)
False 5348
75.9%
True 1695
 
24.1%
2025-08-09T20:12:58.088149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5521 
True
1522 
ValueCountFrequency (%)
False 5521
78.4%
True 1522
 
21.6%
2025-08-09T20:12:58.143379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4678 
True
2365 
ValueCountFrequency (%)
False 4678
66.4%
True 2365
33.6%
2025-08-09T20:12:58.197713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5431 
True
1612 
ValueCountFrequency (%)
False 5431
77.1%
True 1612
 
22.9%
2025-08-09T20:12:58.254191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

MultipleLines_No phone service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
6361 
True
682 
ValueCountFrequency (%)
False 6361
90.3%
True 682
 
9.7%
2025-08-09T20:12:58.310183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

MultipleLines_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4072 
True
2971 
ValueCountFrequency (%)
False 4072
57.8%
True 2971
42.2%
2025-08-09T20:12:58.365638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OnlineSecurity_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.421826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5024 
True
2019 
ValueCountFrequency (%)
False 5024
71.3%
True 2019
28.7%
2025-08-09T20:12:58.476730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OnlineBackup_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.533006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OnlineBackup_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4614 
True
2429 
ValueCountFrequency (%)
False 4614
65.5%
True 2429
34.5%
2025-08-09T20:12:58.588522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DeviceProtection_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.643902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DeviceProtection_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4621 
True
2422 
ValueCountFrequency (%)
False 4621
65.6%
True 2422
34.4%
2025-08-09T20:12:58.700336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

TechSupport_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.757933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4999 
True
2044 
ValueCountFrequency (%)
False 4999
71.0%
True 2044
29.0%
2025-08-09T20:12:58.814207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingTV_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.868937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingTV_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4336 
True
2707 
ValueCountFrequency (%)
False 4336
61.6%
True 2707
38.4%
2025-08-09T20:12:58.924944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingMovies_No internet service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5517 
True
1526 
ValueCountFrequency (%)
False 5517
78.3%
True 1526
 
21.7%
2025-08-09T20:12:58.980296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingMovies_Yes
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4311 
True
2732 
ValueCountFrequency (%)
False 4311
61.2%
True 2732
38.8%
2025-08-09T20:12:59.035697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2025-08-09T20:12:55.285106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:54.880582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.088022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.352546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:54.956276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.156423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.415484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.023005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2025-08-09T20:12:55.220965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2025-08-09T20:12:59.104643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ChurnContract_One yearContract_Two yearDependentsDeviceProtection_No internet serviceDeviceProtection_YesInternetService_Fiber opticInternetService_NoMonthlyChargesMultipleLines_No phone serviceMultipleLines_YesOnlineBackup_No internet serviceOnlineBackup_YesOnlineSecurity_No internet serviceOnlineSecurity_YesPaperlessBillingPartnerPaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed checkPhoneServiceSeniorCitizenStreamingMovies_No internet serviceStreamingMovies_YesStreamingTV_No internet serviceStreamingTV_YesTechSupport_No internet serviceTechSupport_YesTotalChargesgendertenure
Churn1.0000.1770.3020.1630.2270.0650.3070.2270.2760.0000.0380.2270.0810.2270.1700.1910.1500.1330.3010.0910.0000.1500.2270.0600.2270.0620.2270.1640.2130.0000.364
Contract_One year0.1771.0000.2890.0670.0360.1010.0750.0360.1280.0000.0000.0360.0830.0360.0990.0500.0820.0660.1080.0000.0000.0440.0360.0630.0360.0600.0360.0950.2080.0000.307
Contract_Two year0.3020.2891.0000.2040.2180.1640.2110.2180.3030.0000.1050.2180.1100.2180.1910.1470.2470.1720.2820.0000.0000.1160.2180.0730.2180.0710.2180.2400.3960.0000.596
Dependents0.1630.0670.2041.0000.1390.0070.1650.1390.1420.0000.0210.1390.0200.1390.0800.1100.4520.0590.1500.0570.0000.2100.1390.0380.1390.0110.1390.0620.0910.0000.163
DeviceProtection_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
DeviceProtection_Yes0.0650.1010.1640.0070.3801.0000.1750.3800.5190.0700.2000.3800.3030.3800.2750.1030.1530.1110.0000.1870.0700.0580.3800.4020.3800.3900.3800.3330.5220.0000.358
InternetService_Fiber optic0.3070.0750.2110.1650.4650.1751.0000.4650.8520.2890.3660.4650.1650.4650.0280.3260.0000.0480.3360.3060.2890.2550.4650.3220.4650.3290.4650.0160.3700.0000.000
InternetService_No0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
MonthlyCharges0.2760.1280.3030.1420.9670.5190.8520.9671.0000.6640.5260.9670.4880.9670.4250.3600.1560.0560.3180.3770.6640.2340.9670.6650.9670.6680.9670.4450.6380.0070.276
MultipleLines_No phone service0.0000.0000.0000.0000.1710.0700.2890.1710.6641.0000.2790.1710.0500.1710.0920.0110.0120.0000.0000.0000.9990.0000.1710.0300.1710.0190.1710.0950.1520.0000.000
MultipleLines_Yes0.0380.0000.1050.0210.2100.2000.3660.2100.5260.2791.0000.2100.2020.2100.0970.1630.1410.0590.0820.2270.2790.1420.2100.2580.2100.2570.2100.1000.4690.0000.333
OnlineBackup_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
OnlineBackup_Yes0.0810.0830.1100.0200.3810.3030.1650.3810.4880.0500.2020.3811.0000.3810.2830.1260.1410.0900.0000.1730.0500.0650.3810.2740.3810.2820.3810.2940.5080.0060.358
OnlineSecurity_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
OnlineSecurity_Yes0.1700.0990.1910.0800.3330.2750.0280.3330.4250.0920.0970.3330.2830.3331.0000.0000.1420.1150.1110.0800.0920.0360.3330.1870.3330.1750.3330.3540.4200.0120.326
PaperlessBilling0.1910.0500.1470.1100.3200.1030.3260.3200.3600.0110.1630.3200.1260.3200.0001.0000.0080.0060.2080.2050.0110.1560.3200.2110.3200.2230.3200.0360.1590.0000.000
Partner0.1500.0820.2470.4520.0000.1530.0000.0000.1560.0120.1410.0000.1410.0000.1420.0081.0000.0810.0830.0940.0120.0110.0000.1170.0000.1240.0000.1190.3260.0000.378
PaymentMethod_Credit card (automatic)0.1330.0660.1720.0590.0000.1110.0480.0000.0560.0000.0590.0000.0900.0000.1150.0060.0811.0000.3730.2850.0000.0200.0000.0470.0000.0380.0000.1160.1930.0000.234
PaymentMethod_Electronic check0.3010.1080.2820.1500.2840.0000.3360.2840.3180.0000.0820.2840.0000.2840.1110.2080.0830.3731.0000.3870.0000.1710.2840.1370.2840.1440.2840.1140.0800.0000.212
PaymentMethod_Mailed check0.0910.0000.0000.0570.3210.1870.3060.3210.3770.0000.2270.3210.1730.3210.0800.2050.0940.2850.3871.0000.0000.1530.3210.2500.3210.2470.3210.0840.3150.0060.235
PhoneService0.0000.0000.0000.0000.1710.0700.2890.1710.6640.9990.2790.1710.0500.1710.0920.0110.0120.0000.0000.0001.0000.0000.1710.0300.1710.0190.1710.0950.1520.0000.000
SeniorCitizen0.1500.0440.1160.2100.1820.0580.2550.1820.2340.0000.1420.1820.0650.1820.0360.1560.0110.0200.1710.1530.0001.0000.1820.1190.1820.1040.1820.0590.1140.0000.022
StreamingMovies_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
StreamingMovies_Yes0.0600.0630.0730.0380.4180.4020.3220.4180.6650.0300.2580.4180.2740.4180.1870.2110.1170.0470.1370.2500.0300.1190.4181.0000.4180.5330.4180.2790.5180.0000.285
StreamingTV_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
StreamingTV_Yes0.0620.0600.0710.0110.4150.3900.3290.4150.6680.0190.2570.4150.2820.4150.1750.2230.1240.0380.1440.2470.0190.1040.4150.5330.4151.0000.4150.2780.5130.0000.276
TechSupport_No internet service0.2270.0360.2180.1391.0000.3800.4651.0000.9670.1710.2101.0000.3811.0000.3330.3200.0000.0000.2840.3210.1710.1821.0000.4181.0000.4151.0000.3360.4290.0000.023
TechSupport_Yes0.1640.0950.2400.0620.3360.3330.0160.3360.4450.0950.1000.3360.2940.3360.3540.0360.1190.1160.1140.0840.0950.0590.3360.2790.3360.2780.3361.0000.4370.0000.324
TotalCharges0.2130.2080.3960.0910.4290.5220.3700.4290.6380.1520.4690.4290.5080.4290.4200.1590.3260.1930.0800.3150.1520.1140.4290.5180.4290.5130.4290.4371.0000.0000.890
gender0.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0060.0000.0120.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
tenure0.3640.3070.5960.1630.0230.3580.0000.0230.2760.0000.3330.0230.3580.0230.3260.0000.3780.2340.2120.2350.0000.0220.0230.2850.0230.2760.0230.3240.8900.0001.000

Missing values

2025-08-09T20:12:55.533509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-09T20:12:55.936804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

genderSeniorCitizenPartnerDependentstenurePhoneServicePaperlessBillingMonthlyChargesTotalChargesChurnInternetService_Fiber opticInternetService_NoContract_One yearContract_Two yearPaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed checkMultipleLines_No phone serviceMultipleLines_YesOnlineSecurity_No internet serviceOnlineSecurity_YesOnlineBackup_No internet serviceOnlineBackup_YesDeviceProtection_No internet serviceDeviceProtection_YesTechSupport_No internet serviceTechSupport_YesStreamingTV_No internet serviceStreamingTV_YesStreamingMovies_No internet serviceStreamingMovies_Yes
0001010129.8529.850FalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
11000341056.951889.500FalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
2100021153.85108.151FalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
31000450042.301840.750FalseFalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseFalse
4000021170.70151.651TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
5000081199.65820.501TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseTrue
61001221189.101949.400TrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalse
70000100029.75301.900FalseFalseFalseFalseFalseFalseTrueTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
800102811104.803046.051TrueFalseFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueFalseTrueFalseTrue
91001621056.153487.950FalseFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
genderSeniorCitizenPartnerDependentstenurePhoneServicePaperlessBillingMonthlyChargesTotalChargesChurnInternetService_Fiber opticInternetService_NoContract_One yearContract_Two yearPaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed checkMultipleLines_No phone serviceMultipleLines_YesOnlineSecurity_No internet serviceOnlineSecurity_YesOnlineBackup_No internet serviceOnlineBackup_YesDeviceProtection_No internet serviceDeviceProtection_YesTechSupport_No internet serviceTechSupport_YesStreamingTV_No internet serviceStreamingTV_YesStreamingMovies_No internet serviceStreamingMovies_Yes
70331000381169.502625.250TrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
703400006711102.956886.251TrueFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseFalseFalseTrueFalseFalse
70351000191178.701495.100TrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
70360000120060.65743.300FalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrue
70370000721121.151419.400FalseTrueFalseTrueFalseFalseFalseFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse
70381011241184.801990.500FalseFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseTrue
703900117211103.207362.900TrueFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseFalseFalseTrueFalseTrue
70400011110129.60346.450FalseFalseFalseFalseFalseTrueFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
7041111041174.40306.601TrueFalseFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
704210006611105.656844.500TrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseTrueFalseTrueFalseTrue

Duplicate rows

Most frequently occurring

genderSeniorCitizenPartnerDependentstenurePhoneServicePaperlessBillingMonthlyChargesTotalChargesChurnInternetService_Fiber opticInternetService_NoContract_One yearContract_Two yearPaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed checkMultipleLines_No phone serviceMultipleLines_YesOnlineSecurity_No internet serviceOnlineSecurity_YesOnlineBackup_No internet serviceOnlineBackup_YesDeviceProtection_No internet serviceDeviceProtection_YesTechSupport_No internet serviceTechSupport_YesStreamingTV_No internet serviceStreamingTV_YesStreamingMovies_No internet serviceStreamingMovies_Yes# duplicates
8100011020.0520.050FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse3
10100011020.2020.200FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse3
0000011019.5519.550FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse2
1000011019.6519.650FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse2
2000011019.9019.900FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse2
3000011020.9020.901FalseTrueFalseFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalseTrueFalse2
4000011169.2069.201TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2
5000011170.1070.101TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2
6000011170.1570.151TrueFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2
7010011169.6069.601TrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse2

Report generated by YData.

Telco完整业务报告

Telco Customer Churn - Enhanced Business EDA Report

Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations7032
Missing cells0
Missing cells (%)0.0%
Total size in memory1.1 MiB
Average record size in memory170.2 B

Variable types

Categorical16
Numeric5

Alerts

customerID has a high cardinality: 7032 distinct values High cardinality
PhoneService is highly imbalanced (54.2%) Imbalance
customerID has unique values Unique
SeniorCitizen has 5890 (83.8%) zeros Zeros
Churn has 5163 (73.4%) zeros Zeros

Reproduction

Analysis started2025-08-05 04:38:52.759254
Analysis finished2025-08-05 04:38:52.870863
Duration0.11 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

customerID
Categorical

High cardinality  Unique 

Distinct7032
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size786.8 KiB
0002-ORFBO
 
1
6619-RPLQZ
 
1
6629-LADHQ
 
1
6629-CZTTH
 
1
6627-CFOSN
 
1
Other values (7027)
7027 

Unique

Unique7032 ?
Unique (%)100.0%

Sample

1st row7590-VHVEG
2nd row5575-GNVDE
3rd row3668-QPYBK
4th row7795-CFOCW
5th row9237-HQITU

Common Values

ValueCountFrequency (%)
0002-ORFBO 1
 
< 0.1%
6619-RPLQZ 1
 
< 0.1%
6629-LADHQ 1
 
< 0.1%
6629-CZTTH 1
 
< 0.1%
6627-CFOSN 1
 
< 0.1%
6625-UTXEW 1
 
< 0.1%
6625-IUTTT 1
 
< 0.1%
6625-FLENO 1
 
< 0.1%
6624-JDRDS 1
 
< 0.1%
6621-YOBKI 1
 
< 0.1%
Other values (7022) 7022
99.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Male
3549 
Female
3483 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3549
50.5%
Female 3483
49.5%

Common Values (Plot)

2025-08-05T14:38:52.934675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SeniorCitizen
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1624004551
Minimum0
Maximum1
Zeros5890
Zeros (%)83.8%
Negative0
Negative (%)0.0%
Memory size109.9 KiB
2025-08-05T14:38:52.987644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3688439968
Coefficient of variation (CV)2.271200512
Kurtosis1.353321348
Mean0.1624004551
Median Absolute Deviation (MAD)0
Skewness1.831102544
Sum1142
Variance0.1360458939
MonotonicityNot monotonic
2025-08-05T14:38:53.037083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 5890
83.8%
1 1142
 
16.2%
ValueCountFrequency (%)
0 5890
83.8%
1 1142
 
16.2%
ValueCountFrequency (%)
1 1142
 
16.2%
0 5890
83.8%

Partner
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
No
3639 
Yes
3393 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 3639
51.7%
Yes 3393
48.3%

Common Values (Plot)

2025-08-05T14:38:53.096304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Dependents
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
No
4933 
Yes
2099 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 4933
70.2%
Yes 2099
29.8%

Common Values (Plot)

2025-08-05T14:38:53.150596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

tenure
Real number (ℝ)

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.42178612
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.9 KiB
2025-08-05T14:38:53.212026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.54525971
Coefficient of variation (CV)0.7570606881
Kurtosis-1.38782258
Mean32.42178612
Median Absolute Deviation (MAD)22
Skewness0.2377308319
Sum227990
Variance602.4697742
MonotonicityNot monotonic
2025-08-05T14:38:53.284582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
8 123
 
1.7%
70 119
 
1.7%
Other values (62) 4767
67.8%
ValueCountFrequency (%)
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.4%
68 100
 
1.4%

PhoneService
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Yes
6352 
No
680 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 6352
90.3%
No 680
 
9.7%

Common Values (Plot)

2025-08-05T14:38:53.355222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

MultipleLines
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
3385 
Yes
2967 
No phone service
680 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo phone service
2nd rowNo
3rd rowNo
4th rowNo phone service
5th rowNo

Common Values

ValueCountFrequency (%)
No 3385
48.1%
Yes 2967
42.2%
No phone service 680
 
9.7%

Common Values (Plot)

2025-08-05T14:38:53.411810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

InternetService
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Fiber optic
3096 
DSL
2416 
No
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSL
2nd rowDSL
3rd rowDSL
4th rowDSL
5th rowFiber optic

Common Values

ValueCountFrequency (%)
Fiber optic 3096
44.0%
DSL 2416
34.4%
No 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.472741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OnlineSecurity
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
3497 
Yes
2015 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 3497
49.7%
Yes 2015
28.7%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.533817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

OnlineBackup
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
3087 
Yes
2425 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 3087
43.9%
Yes 2425
34.5%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.595119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DeviceProtection
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
3094 
Yes
2418 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 3094
44.0%
Yes 2418
34.4%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.840929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

TechSupport
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
3472 
Yes
2040 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 3472
49.4%
Yes 2040
29.0%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.901970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingTV
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
2809 
Yes
2703 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 2809
39.9%
Yes 2703
38.4%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:53.962651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

StreamingMovies
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
No
2781 
Yes
2731 
No internet service
1520 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 2781
39.5%
Yes 2731
38.8%
No internet service 1520
21.6%

Common Values (Plot)

2025-08-05T14:38:54.024398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.1 KiB
Month-to-month
3875 
Two year
1685 
One year
1472 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-month
2nd rowOne year
3rd rowMonth-to-month
4th rowOne year
5th rowMonth-to-month

Common Values

ValueCountFrequency (%)
Month-to-month 3875
55.1%
Two year 1685
24.0%
One year 1472
 
20.9%

Common Values (Plot)

2025-08-05T14:38:54.086989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PaperlessBilling
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Yes
4168 
No
2864 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 4168
59.3%
No 2864
40.7%

Common Values (Plot)

2025-08-05T14:38:54.148465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PaymentMethod
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.3 KiB
Electronic check
2365 
Mailed check
1604 
Bank transfer (automatic)
1542 
Credit card (automatic)
1521 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElectronic check
2nd rowMailed check
3rd rowMailed check
4th rowBank transfer (automatic)
5th rowElectronic check

Common Values

ValueCountFrequency (%)
Electronic check 2365
33.6%
Mailed check 1604
22.8%
Bank transfer (automatic) 1542
21.9%
Credit card (automatic) 1521
21.6%

Common Values (Plot)

2025-08-05T14:38:54.208438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

MonthlyCharges
Real number (ℝ)

Distinct1584
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.79820819
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.9 KiB
2025-08-05T14:38:54.281862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5875
median70.35
Q389.8625
95-th percentile107.4225
Maximum118.75
Range100.5
Interquartile range (IQR)54.275

Descriptive statistics

Standard deviation30.08597388
Coefficient of variation (CV)0.464302559
Kurtosis-1.256156424
Mean64.79820819
Median Absolute Deviation (MAD)24.05
Skewness-0.2221029277
Sum455661
Variance905.1658246
MonotonicityNot monotonic
2025-08-05T14:38:54.356962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 61
 
0.9%
19.9 44
 
0.6%
19.95 44
 
0.6%
19.85 44
 
0.6%
19.65 43
 
0.6%
20 42
 
0.6%
19.7 42
 
0.6%
20.15 40
 
0.6%
19.55 40
 
0.6%
19.75 39
 
0.6%
Other values (1574) 6593
93.8%
ValueCountFrequency (%)
18.25 1
< 0.1%
18.4 1
< 0.1%
18.55 1
< 0.1%
18.7 2
< 0.1%
18.75 1
< 0.1%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%

TotalCharges
Real number (ℝ)

Distinct6530
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2283.300441
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.9 KiB
2025-08-05T14:38:54.432124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.605
Q1401.45
median1397.475
Q33794.7375
95-th percentile6923.59
Maximum8684.8
Range8666
Interquartile range (IQR)3393.2875

Descriptive statistics

Standard deviation2266.771362
Coefficient of variation (CV)0.992760883
Kurtosis-0.2317987609
Mean2283.300441
Median Absolute Deviation (MAD)1222.8
Skewness0.9616424997
Sum16056168.7
Variance5138252.407
MonotonicityNot monotonic
2025-08-05T14:38:54.504012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
20.05 8
 
0.1%
19.9 8
 
0.1%
19.65 8
 
0.1%
19.55 7
 
0.1%
45.3 7
 
0.1%
19.45 6
 
0.1%
20.25 6
 
0.1%
20.15 6
 
0.1%
Other values (6520) 6956
98.9%
ValueCountFrequency (%)
18.8 1
< 0.1%
18.85 2
< 0.1%
18.9 1
< 0.1%
19 1
< 0.1%
19.05 1
< 0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%

Churn
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2657849829
Minimum0
Maximum1
Zeros5163
Zeros (%)73.4%
Negative0
Negative (%)0.0%
Memory size109.9 KiB
2025-08-05T14:38:54.561479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4417817114
Coefficient of variation (CV)1.662177097
Kurtosis-0.8753305007
Mean0.2657849829
Median Absolute Deviation (MAD)0
Skewness1.060621769
Sum1869
Variance0.1951710805
MonotonicityNot monotonic
2025-08-05T14:38:54.608522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 5163
73.4%
1 1869
 
26.6%
ValueCountFrequency (%)
0 5163
73.4%
1 1869
 
26.6%
ValueCountFrequency (%)
1 1869
 
26.6%
0 5163
73.4%

Report generated by YData.

深度商业EDA报告(含 ROI & Harvard 引用)

以下商业洞察基于 Profiling 数据分析结果,结合行业研究与 ROI 模拟,为企业客户保留策略提供决策支持。

客户流失现状与数据质量

Telco 年 churn 率约为 26.5%,显著高于行业平均水平。CustomerGauge (2024) 指出电信行业 churn 范围普遍在 20%–50%,全球平均约为 21.5%。此外,TotalCharges 缺失主要集中在 tenure=0 的新客户,这与 Wu et al. (2024) 的研究一致,说明初期体验是决定长期留存的关键因素。

流失率分布

合同类型(Contract)与 churn 机制分析

月度合约客户 churn 率高达 43%,而长期合约客户仅约 15%。行业调研表明,首月优惠结束后 churn 显著增加 (TechSee, 2019; CustomerGauge, 2024)。策略建议包括:1)向月度合约客户提供续签折扣与增值服务捆绑;2)设计‘软解约成本’如礼品和优惠券。建模验证将通过逻辑回归评估若 20% 月度客户转签长期合约,整体 churn 是否下降 ≥5%。

支付方式与流失率

支付方式与 churn

电子支票客户 churn 率约为 45%,自动扣款/信用卡用户 churn 率低于 20%。研究表明支付体验差是 churn 的主要驱动之一 (TechSee, 2019; Ribeiro et al., 2023)。策略建议:1)为电子支票用户提供迁移至自动扣款的激励(如账单减免或积分返现);2)建立自动扣款积分计划提升忠诚度。建模验证将检验支付方式特征的重要性,并模拟迁移 30% 用户的效果。

月费与流失关系

月费水平与敏感性

月费超过 $80 的客户 churn 率超过 50%,低于 $40 的客户 churn 率不足 15%。Wu et al. (2024) 指出高账单客户 churn 风险显著更高,对性价比敏感。策略建议:1)为高账单客户提供 VIP 支持与快速响应;2)推出差异化服务捆绑。建模验证将分箱 MonthlyCharges,模拟高账单 churn 降低 15% 的整体改善效果。

任期与流失率

新客户(Tenure)与早期 churn 风险

tenure < 12 个月客户 churn 率达 55%,而 tenure > 24 个月仅约 15%。Wu et al. (2024) 研究指出,首年体验期是 churn 风险最高阶段。策略建议:1)实施 Onboarding 策略,如前三个月免费技术支持;2)提供增值服务免费试用。建模验证将检验若 30% 新客户留存,整体 churn 改善。

增值服务与流失率

增值服务绑定与留存

未订购增值服务的客户 churn 率约 50%,订购客户 churn 率低于 20%。Ribeiro et al. (2023) 的系统文献表明,增值服务显著降低 churn。策略建议:1)推行‘3 个月免费试用 → 自动续订’机制;2)将服务与长期合同捆绑。建模验证将检验若订购率提升 20%,整体 churn 改善。

TotalCharges 与流失率

参考文献(Harvard 格式)

Python代码解析

Python项目